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CREATION
Title : Automatic Extractive Text Summarization for Indonesian News Articles Using Maximal Marginal Relevance and Non-Negative Matrix Factorization
Author :

INGGAR RIYANDI MUSYAFFANTO (1) Guntur Budi Herwanto, S.Kom., M.Cs. (2) Dr. Mardhani Riasetiawan, SE Ak, M.T. (3)
Date : 0 2019
Keyword : text summarization,extractive,maximal marginal relevance,non-negative matrix factorization,news text summarization,extractive,maximal marginal relevance,non-negative matrix factorization,news
Abstract : The rapid development of the internet has led to the creation of many online news sites which results in abundance options of news content. Competition between the publishers usually will lead to the making of interesting and often misleading titles that are not conveyed the content of the news itself. This problem can make it difficult for the readers because they usually click news based on its title. To overcome this problem, we have developed a text summarization that extracts the keyword from the content, then used that keyword to make a summary. This research combines Maximal Marginal Relevance (MMR) and Nonnegative Matrix Factorization (NMF) to build automatic extractive text summarized. MMR used to summarize the text automatically and NMF is used to extract the keyword to form the query for MMR. We compare the result with the human summary and measure it using ROUGE-N score. Based on our experiment, the performance of the summarizer system that combines NMF as the keyword generator produce better performance in ROUGE-1 and ROUGE-2 compared to systems that only use a title with MMR. We also perform an experiment to see the impact on the number of a keyword in the summary result. We found that 8 keywords are the ideal number of keywords to represents the idea of the main text.
Group of Knowledge :
Level : Internasional
Status :
Published
Document
No Title Document Type Action
1 Automatic Extractive Text Summarization for Indonesian News Articles Using Maximal Marginal Relevance and Non-Negative Matrix Factorization.pdf
Document Type : Cek Similarity
Cek Similarity View
2 Automatic Extractive Text Summarization for Indonesian News Articles Using Maximal Marginal Relevance and Non-Negative Matrix Factorization.pdf
Document Type : [PAK] Full Dokumen
[PAK] Full Dokumen View
3 sertifikat ICST2019-Guntur Budi-Mardhani-etal.pdf
Document Type : [PAK] Sertifikat Seminar
[PAK] Sertifikat Seminar View
4 prosiding_ICST2019.pdf
Document Type : [PAK] Full Dokumen
[PAK] Full Dokumen View
5 Automatic extractive text summarization for.pdf
Document Type : [PAK] Full Dokumen
[PAK] Full Dokumen View